Annotation free license plate recognition method and system

ABSTRACT

Methods and systems for recognizing a license plate character. Synthetic license plate character images are generated for a target jurisdiction. A limited set of license plate images can be captured for a target jurisdiction utilizing an image-capturing unit. The license plate images are then segmented into license plate character images for the target jurisdiction. The license plate character images collected for the target jurisdiction can be manually labeled. A domain adaptation technique can be utilized to reduce the divergence between synthetically generated and manually labeled target jurisdiction image sets. Additionally, OCR classifiers are trained utilizing the images after the domain adaptation method has been applied. One or more input license plate character images can then be received from the target jurisdiction. Finally, the trained OCR classifier can be employed to determine the most likely labeling for the character image and a confidence associated with the label.

CROSS-REFERENCE TO PROVISIONAL APPLICATION

This application claims priority under 35 U.S.C. 119(e) to U.S.Provisional Patent Application Ser. No. 62/102,692, entitled “AnnotationFree License Plate Recognition Method and System,” which was filed onJan. 13, 2015, the disclosure of which is incorporated herein byreference in its entirety.

TECHNICAL FIELD

Embodiments are generally related to the field of ALPR (AutomaticLicense Plate Recognition). Embodiments also relate to techniques andsystems for character identification and extraction from images.Embodiments additionally relate to the field of OCR (Optical CharacterRecognition).

BACKGROUND OF THE INVENTION

ALPR is a mature technology extensively employed in intelligenttransportation systems for applications such as automated tolling, lawenforcement, and parking management, among others. These systemstypically include four modules: a) image acquisition, b) license platelocalization, c) character segmentation (i.e., extracting images of eachindividual character in the license plate), and d) characterrecognition. A number of alternative methods, however, have beenproposed for license plate recognition.

ALPR methods typically require an offline phase to train an OCR enginebefore deployment. In this offline phase, a classifier is trained foreach character in a one-vs-all fashion using a set of manually annotatedcharacter samples. In order to match the distribution of training andtarget data sets, data collection and manual annotation is repeated foreach country/state that font is different, and for each site that camerasettings/configuration/geometry varies. Considering enormous variety ofplate samples (i.e., variations in plate design, font, or layout),camera configuration, and geometries, manual annotation results inexcessive operational cost and overhead and hence, poses an importantchallenge for the scalability of ALPR systems.

Efforts have been made to develop automated license plate recognitionsystems and some implementations have been successfully rolled out insome U.S. states (e.g., CA, NY, etc.). One module type employed in someautomated license plate recognition systems includes trainingclassifiers for character recognition, commonly employed after detectinga license plate in a license plate image and segmenting out thecharacters from the localized plate region.

A classifier can be trained for each character in a one vs. all fashionusing samples collected from the site, wherein an operator manuallylabels the collected samples. Considering the high accuracy (i.e., 99%)required by our customers for the overall recognition system, theclassifiers are typically trained using on the order of approximately1000 manually labeled samples per character. The substantial time andeffort required for manual annotation of training images can result inexcessive operational costs and increased overhead. This problem isexacerbated for jurisdictions requiring multiple OCR engines (e.g., onefor each of the most common states), as the annotation burden growsquickly (e.g., 36 symbols×1000 samples×6 jurisdictions=216 k samples tomanually label).

In order to address this problem, some solutions have proposed trainingclassifiers based on synthetically generated samples. Instead ofcollecting samples from the site, training images are syntheticallygenerated using the font and layout of the State of interest. FIG. 1,for example, illustrates a block diagram of a plate synthesis workflow1. In the prior art configuration shown in FIG. 1, a blank license plateimage is shown, which is provided to a text overlay module 5. Renderingeffects 13 (e.g., font, spacing, layout, shadow/emboss, etc.) are alsoprovided to the text overlay module 5, along with output from acharacter sequence generation module 7. Examples of character sequencegeneration data are shown in box 9 to the right of the charactersequence generation module. State rules 15 for valid sequences can alsobe provide as input to module 7. License plate images 11 are output fromthe text overlay module 5. An image distortion model, which includescolor-to-IR conversion, image noise, brightness, geometric distortions,etc., can be also fitted on synthesized images to mimic the impact ofcapturing vehicle plate images with a real video camera system.

While such methods can eliminate manual interference required fortraining, they usually result in deterioration in the classificationaccuracy. FIG. 2, for example, illustrates a graph 2 of accuracy-yieldcurves for classifiers trained using only synthetic (green curve) andreal images (red curve). In the example graph 2 of FIG. 2, even though2000 synthetic images are used per character in training, the accuracyat the same yield is lower when classifiers are trained with 1500 realsamples per character.

While these methods eliminate manual interference required for training,they usually result in deterioration in the classification accuracy.What is needed is a solution that minimizes manual annotation requiredfor training classifiers while having minimal/no impact on theclassification accuracy.

BRIEF SUMMARY

The following summary is provided to facilitate an understanding of someof the innovative features unique to the disclosed embodiments and isnot intended to be a full description. A full appreciation of thevarious aspects of the embodiments disclosed herein can be gained bytaking the entire specification, claims, drawings, and abstract as awhole.

It is, therefore, one aspect of the disclosed embodiments to provide forimproved ALPR methods and systems.

It is another aspect of the disclosed embodiments to provide forannotation free license plate recognition methods and systems.

The aforementioned aspects and other objectives and advantages can nowbe achieved as described herein. Methods and systems for recognizing alicense plate character are disclosed. In an offline training phase, astep or operation can be implemented for generating synthetic licenseplate character images for a target jurisdiction. Also in an offlinetraining phase, a step or operation can be implemented for capturing alimited set of license plate images for a target jurisdiction utilizingan image-capturing unit and thereafter segmenting the license plateimages into license plate character images for the target jurisdiction.Additionally, in an offline training phase, the license plate characterimages collected for the target jurisdiction can be manually labeled.Also in an offline training phase, a step or logical operation can beimplemented for applying a domain adaptation method to reduce thedivergence between the synthetically generated and manually labeledtarget jurisdiction image sets. Additionally, in an offline trainingphase, a step or operation can be implemented for training a set of OCRclassifiers using the images after the domain adaptation method has beenapplied. In an online classification phase, a step or logical operationcan be provided for receiving at least one input license plate characterimage from the target jurisdiction. The trained OCR classifier can beemployed to determine the most likely labeling for the character imageand a confidence associated with the label.

The disclosed embodiments thus attempt to minimize manual annotationrequired for training an OCR engine in an ALPR system. In the offlinephase, either artificially generated synthetic license plate images orcharacter samples acquired by OCR engines already trained in anoperating system can be utilized. Training the OCR engine usingcharacter samples that are different from the images acquired from theactual camera capture site causes a mismatch between training and targetdata distributions, which causes deterioration in the OCR performance inthe field. In order to improve the OCR performance and match trainingand target data distributions, an unsupervised domain adaptation can beapplied via subspace and dictionary learning.

In the domain adaptation, a set of labeled samples can be employed foreach character from the training set and a set of unlabeled charactersamples acquired from the actual camera site. The unlabeled charactersamples can be extracted using the generic license plate localizationand character segmentation modules that are typically independent ofcharacter font, license plate lay-out, etc. The domain adaptationestimates the domain shift between training and target images andgenerates a shared feature representation across training and targetsamples. One-vs-all classifiers for the OCR engine are trained using theshared feature representation space. The present inventors haveconducted experiments on artificially generated and actual charactersamples collected, which demonstrate the efficiency of the disclosedapproach.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying figures, in which like reference numerals refer toidentical or functionally-similar elements throughout the separate viewsand which are incorporated in and form a part of the specification,further illustrate the present invention and, together with the detaileddescription of the invention, serve to explain the principles of thepresent invention.

FIG. 1 illustrates a block diagram of a prior art plate synthesisworkflow;

FIG. 2, for example, illustrates a prior art graph of accuracy-yieldcurves for classifiers trained using only synthetic and real images;

FIG. 3 illustrates a block diagram of basic components of an annotationfree license plate recognition system, in accordance with a preferredembodiment;

FIG. 4 illustrates a graph depicting accuracy-yield curves for trainingSVM classifiers using synthetic and real images, in accordance with analternative embodiment;

FIG. 5. illustrates a graph depicting the accuracy-yield curves fortraining the SVM classifier using CA images and NY images for NY plates,in accordance with an alternative embodiment;

FIG. 6 illustrates a schematic view of a computer system, in accordancewith an embodiment;

FIG. 7 illustrates a schematic view of a software system including amodule, an operating system, and a user interface, in accordance with anembodiment; and

FIG. 8 illustrates a high-level flow chart of operations depictinglogical operations of a method for annotation free method forrecognizing a license plate character, in accordance with a preferredembodiment.

DETAILED DESCRIPTION

The particular values and configurations discussed in these non-limitingexamples can be varied and are cited merely to illustrate at least oneembodiment and are not intended to limit the scope thereof.

Automated license plate recognition (ALPR) is a key capability intransportation imaging applications including tolling, enforcement, andparking, among others. An important module in ALPR systems is imageclassification that includes training classifiers for characterrecognition, commonly employed after detecting a license plate in alicense plate image and segmenting out the characters from the localizedplate region. A classifier is trained for each character in a one-vs-allfashion using segmented character samples collected from the actualcamera capture site, where the collected samples can be manually labeledby an operator. The substantial time and effort required for manualannotation of training images can result in excessive operational costand overhead. In this paper, we propose a new method to minimize manualannotation required for training classifiers in an ALPR system.

Instead of collecting training images from the actual camera capturesite, the disclosed approach utilizes either artificially generatedsynthetic license plate images or character samples acquired by trainedALPR systems already operating in other sites. The performance gap dueto differences between training and target domain distributions isminimized using an unsupervised domain adaptation.

FIG. 3 illustrates a block diagram of basic components of an annotationfree license plate recognition system 10, in accordance with a preferredembodiment. The disclosed embodiments offer a new method and system thatminimizes and/or eliminates manual annotation required for trainingclassifiers in an ALPR (Automatic License Plate Recognition) system. Theclassifiers can be trained either using synthetically generatedcharacters or using samples acquired by trained ALPR systems operatingin other States. System 10 includes one or more module(s) 252, which caninclude, for example, a generation module 12 for generating syntheticimages for each character using the font and layout of the State ofinterest along with a collection module 14 for collecting charactersamples from other States/countries that trained ALPR systems areoperating. System 10 also includes an identification module 16 foridentifying the source domain (synthetic images or samples obtained fromother States) that best matches with the character samples from theState of interest (target domain). System 10 further includes a reducingmodule 18 for reducing divergence between the best-matched source domainand target domain to train one-vs-all classifiers. The module(s) 252 cancommunicate with, for example, a processor 201 and a memory 207, asdiscussed in greater detail herein with respect to FIGS. 6 and 7.

The use of such modules reduces the time and effort required forgathering images and training OCR engine in deployment of ALPR systemsin new jurisdictions. The disclosed embodiments also can significantlyreduce deployment costs while rendering the training investment morepredictable.

The generation module 12 generates synthetic images for each character.The synthetic images can be generated following the methodologydescribed in Ref. 2. The collection module 14 collects character sampleother States/countries that trained ALPR systems are operating. That is,automated license plate recognition systems have already been operatingin several states and countries. Thus, already operating ALPR systemsprovide an opportunity to collect training samples for deployments innew jurisdictions. Character images collected from CA or NY, forexample, may be used as training images for a deployment in a newjurisdiction.

The identification module 16 identifies the source domain (syntheticimages or samples obtained from other States) that best matches with thecharacter samples from the State of interest (target domain). Aftercollecting/generating training images from various source domains (i.e.,creating synthetic images and collecting samples from otherjurisdictions), the next challenge is to determine which source domainto use in training to achieve the best performance when classifiers aretested on target domain samples. Ideally, this question can easily beanswered if there were time and resources to collect labeled testsamples from the target domain on which classifiers trained by differentsource domain samples are tested and the best one is picked. Butcollecting labeled samples from target domain (new jurisdiction) is timeconsuming and costly. Alternatively, the best source domain can bedetermined based on the similarity between the source and targetdomains, which can be determined based on their distributions or domainshifts between the source and target domains.

The reducing module 18 can be implemented for applying domain adaptationbetween the best-matched source domain and target domain to trainone-vs-all classifiers. Once the best source domain is selected, domainadaptation is applied to match the distributions of source and targetdomains. If labeled samples are only available in the source domain, anunsupervised domain adaptation technique using manifold learning orsubspace interpolation via dictionary learning can be employed. If somelabeled samples are also available in the target domain, asemi-supervised domain adaptation technique based on metric learning canbe employed. In any case, applying domain adaptation between source andtarget domains reduces the divergence between two domains.

The present inventors have tested the performance of the disclosedembodiments to demonstrate the benefit and feasibility of the invention.In one experiment, CA plates were used and 2500 real samples collectedfor each character. 2000 synthetic images were also generated percharacter using the methodology described in, for example, “Imagesimulation for automatic license plate recognition,” Bala, Raja, et al.IS&T/SPIE Electronic Imaging. International Society for Optics andPhotonics, 2012, which is incorporated herein by reference. 1500 out of2500 real sample per character can be used, for example, to train theclassifier and the rest 1000 samples are used for testing. The imagescan be scaled to 48×24 before feature extraction.

HOG features were extracted and linear SVM classifiers trained in aone-vs-all fashion for each character for both synthetic and realimages. After feature extraction applied adaptation was applied usingsubspace interpolation via dictionary learning on source and targetdomains.

FIG. 4 illustrates graph 40 depicting accuracy-yield curves for trainingthe SVM classifiers using synthetic (green curve) and real images (redcurve), in accordance with an alternative embodiment. In the example ofgraph 40, applying domain adaptation on source (synthetic) and target(real) domains improves the performance (cyan curve). Including sometarget domain samples in training along with applying domain adaptationfurther improves the performance (blue and magenta curves).

In order to validate results, further, another experiment was performedon NY plates. In this case, CA images were employed as the source domainand NY images as the target and the first experiment was then repeated.

FIG. 5 illustrates a graph 50 depicting the accuracy-yield curves fortraining the SVM classifier using CA images (red curve) and NY images(green curve) for NY plates. Applying domain adaptation on source andtarget domains improves the performance (cyan curve). Including sometarget domain samples in training along with applying domain adaptationfurther improves the performance (blue, black, yellow, and magentacurves).

As can be appreciated by one skilled in the art, embodiments can beimplemented in the context of a method, data processing system, orcomputer program product. Accordingly, embodiments may take the form ofan entire hardware embodiment, an entire software embodiment, or anembodiment combining software and hardware aspects all generallyreferred to herein as a “circuit” or “module.” Furthermore, embodimentsmay in some cases take the form of a computer program product on acomputer-usable storage medium having computer-usable program codeembodied in the medium. Any suitable computer readable medium may beutilized including hard disks, USB Flash Drives, DVDs, CD-ROMs, opticalstorage devices, magnetic storage devices, server orage, databases, etc.

Computer program code for carrying out operations of the presentinvention may be written in an object oriented programming language(e.g., Java, C++, etc.). The computer program code, however, forcarrying out operations of particular embodiments may also be written inconventional procedural programming languages, such as the “C”programming language or in a visually oriented programming environment,such as, for example, Visual Basic.

The program code may execute entirely on the user's computer, partly onthe user's computer, as a stand-alone software package, partly on theuser's computer and partly on a remote computer, or entirely on theremote computer. In the latter scenario, the remote computer may beconnected to a user's computer through a local area network (LAN) or awide area network (WAN), wireless data network, e.g., Wi-Fi, Wimax,802.xx, and cellular network or the connection may be made to anexternal computer via most third party supported networks (for example,through the Internet utilizing an Internet Service Provider).

The embodiments are described at least in part herein with reference toflowchart illustrations and/or block diagrams of methods, systems, andcomputer program products and data structures according to embodimentsof the invention. It will be understood that each block of theillustrations, and combinations of blocks, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general-purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the function/acts specified inthe block or blocks.

These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instruction meanswhich implement the function/act specified in the various block orblocks, flowcharts, and other architecture illustrated and describedherein.

The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer implemented process such that theinstructions which execute on the computer or other programmableapparatus provide steps for implementing the functions/acts specified inthe block or blocks.

FIGS. 6-7 are shown only as exemplary diagrams of data-processingenvironments in which embodiments may be implemented. It should beappreciated that FIGS. 6-7 are only exemplary and are not intended toassert or imply any limitation with regard to the environments in whichaspects or embodiments of the disclosed embodiments may be implemented.Many modifications to the depicted environments may be made withoutdeparting from the spirit and scope of the disclosed embodiments.

As illustrated in FIG. 6, some embodiments may be implemented in thecontext of a data-processing system 200 that can include one or moreprocessors such as processor 201, a memory 202, an input/outputcontroller 203, a peripheral USB—Universal Serial Bus connection 208, akeyboard 204, an input device 205 (e.g., a pointing device such as amouse, track ball, pen device, etc.), a display 206, and in some cases,mass storage 207. In some embodiments, the system 200 can communicatewith a rendering device, such as, for example, an image-capturing unit209 (e.g., camera) that captures images in, for example, an ALPR system.

As illustrated, the various components of data-processing system 200 cancommunicate electronically through a system bus 210 or similararchitecture. The system bus 210 may be, for example, a subsystem thattransfers data between, for example, computer components withindata-processing system 200 or to and from other data-processing devices,components, computers, etc. Data-processing system 200 may beimplemented as, for example, a server in a client-server based network(e.g., the Internet) or can be implemented in the context of a clientand a server (i.e., where aspects are practiced on the client and theserver). Data-processing system 200 may be, for example, a standalonedesktop computer, a laptop computer, a Smartphone, a pad computingdevice, and so on.

FIG. 7 illustrates a computer software system 250 for directing theoperation of the data-processing system 200 depicted in FIG. 6. Thesoftware application 254, stored for example in memory 202, generallyincludes a kernel or operating system 251 and a shell or interface 253.One or more application programs, such as software application 254, maybe “loaded” (i.e., transferred from, for example, mass storage 207 orother memory location into the memory 202) for execution by thedata-processing system 200. The data-processing system 200 can receiveuser commands and data through the interface 253; these inputs may thenbe acted upon by the data-processing system 200 in accordance withinstructions from operating system 251 and/or software application 254.The interface 253 in some embodiments can serve to display results,whereupon a user 249 may supply additional inputs or terminate asession. The software application 254 can include a module(s) 252 thatcan, for example, implement instructions or operations such as thoseshown in FIG. 3 and FIG. 8 herein. In some example embodiments, module252 may function as an image-processing module.

The following discussion is intended to provide a brief, generaldescription of suitable computing environments in which the system andmethod may be implemented. Although not required, the disclosedembodiments will be described in the general context ofcomputer-executable instructions, such as program modules, beingexecuted by a single computer. In most instances, a “module” constitutesa software application.

Generally, program modules include, but are not limited to, routines,subroutines, software applications, programs, objects, components, datastructures, etc., that perform particular tasks or implement particularabstract data types and instructions. Moreover, those skilled in the artwill appreciate that the disclosed method and system may be practicedwith other computer system configurations, such as, for example,hand-held devices, multi-processor systems, data networks,microprocessor-based or programmable consumer electronics, networkedPCs, minicomputers, mainframe computers, servers, and the like.

Note that the term module as utilized herein may refer to a collectionof routines and data structures that perform a particular task orimplements a particular abstract data type. Modules may be composed oftwo parts: an interface, which lists the constants, data types,variable, and routines that can be accessed by other modules orroutines, and an implementation, which is typically private (accessibleonly to that module) and which includes source code that actuallyimplements the routines in the module. The term module may also simplyrefer to an application, such as a computer program designed to assistin the performance of a specific task, such as word processing,accounting, inventory management, etc. Thus, a module can be implementedto, for example, implement the instructions shown in FIG. 8.

FIGS. 6-7 are thus intended as examples and not as architecturallimitations of disclosed embodiments. Additionally, such embodiments arenot limited to any particular application or computing or dataprocessing environment. Instead, those skilled in the art willappreciate that the disclosed approach may be advantageously applied toa variety of systems and application software. Moreover, the disclosedembodiments can be embodied on a variety of different computingplatforms, including Macintosh, UNIX, LINUX, and the like.

FIG. 8 illustrates a high-level flow chart of operations depictinglogical operations of a method 300 for annotation free method forrecognizing a license plate character, in accordance with a preferredembodiment. As indicated at block 301, the process can be initiated.Thereafter, as shown at block 302, a step or operation can beimplemented in an offline training phase for generating syntheticlicense plate character images for a target jurisdiction.

Next, as described at block 304, a step or operation can be processed inan offline training phase for capturing a limited set of license plateimages for a target jurisdiction utilizing an image-capturing unit(e.g., camera 209 shown in FIG. 6) and thereafter segmenting the licenseplate images into license plate character images for the targetjurisdiction. Then, as indicated at block 306, in an offline trainingphase, a step or operation can be provided for manually labeling thelicense plate character images collected for the target jurisdiction.Thereafter, as depicted at block 308, in an offline training phase, astep or operation can be implemented for applying a domain adaptationmethod to reduce the divergence between the synthetically generated andmanually labeled target jurisdiction image sets.

As indicated previously, in the domain adaptation, a set of labeledsamples can be employed for each character from the training set and aset of unlabeled character samples acquired from the actual camera site.The unlabeled character samples can be extracted using the genericlicense plate localization and character segmentation modules that aretypically independent of character font, license plate lay-out, etc. Thedomain adaptation estimates the domain shift between training and targetimages and generates a shared feature representation across training andtarget samples.

Next, as depicted at block 310, in an offline training phase, a step orlogical operation can be provided for training a set of OCR classifiersusing the images after the domain adaptation method has been applied.Then, as illustrated at block 312, in an online classification phase, astep or operation can be provided for receiving at least one inputlicense plate character image from the target jurisdiction. Thereafter,as described at block 314, a step or operation can be implemented forusing the trained OCR classifiers to determine the most likely labelingfor the character image and a confidence associated with the label. Theprocess then ends, as shown at block 316.

The disclosed embodiments address the issue of minimizing manualannotation and data collection required for the scalability of ALPRsystems in new jurisdictions/countries. When an OCR engine is trainedusing either artificially generated synthetic images or charactersamples acquired from other sites, the OCR performance in the field isdegraded due to the mismatch between training and target datadistributions. An unsupervised domain adaptation estimates the domainshift between training and target domains and improves the OCRperformance without requiring manual annotation in new deployments. Theestimated domain shifts between target and multiple source domainsenable to select the domain that yields the best OCR performance.

It is important to keep in mind that digit character images obtainedfrom artificially generated license plates and from actual cameracapture site seem similar. That is, even though synthetic characterimages look similar to actual images in general, the distortion can bequite broad in the real images which is hard to model and estimate inthe synthetic image generation process. The slight difference betweensynthetic and real character images in terms of camera distortion causesa mismatch between distributions of synthetic and real images. Asindicated previously, when OCR classifiers are trained using syntheticimages, a notable performance loss is observed in the recognitionaccuracy due to this mismatch.

It will be appreciated that variations of the above-disclosed and otherfeatures and functions, or alternatives thereof, may be desirablycombined into many other different systems or applications. It will alsobe appreciated that various presently unforeseen or unanticipatedalternatives, modifications, variations or improvements therein may besubsequently made by those skilled in the art which are also intended tobe encompassed by the following claims.

What is claimed is:
 1. A method for recognizing a license platecharacter, said method comprising: applying a domain adaptation in atleast one offline training phase to reduce a divergence betweensynthetic license plate character images that are syntheticallygenerated and license plate character images manually labeled for atarget jurisdiction; training in said at least one offline trainingphase a set of OCR classifiers using said license plate character imagesafter said domain adaptation has been applied; receiving in an onlineclassification phase at least one input license plate character imagefrom said target jurisdiction; and determining a most likely label for acharacter image and a confidence associated with said label utilizingsaid trained OCR classifiers, thereby minimizing manual labelingrequired for training an OCR engine in a license plate recognitionsystem.
 2. The method of claim 1 further comprising generating in saidleast one offline training phase said synthetic license plate characterimages for said target jurisdiction.
 3. The method of claim 1 furthercomprising manually labeling in said at least one offline training phasesaid license plate character images collected for said targetjurisdiction.
 4. The method of claim 1 further comprising: capturing insaid at least one offline training phase a limited set of license plateimages for said target jurisdiction, said limited set of license platesimages captured utilizing an image capturing unit.
 5. The method ofclaim 4 further comprising: thereafter segmenting license plate imagesamong said limited set of license plate images into license platecharacter images for said target jurisdiction.
 6. The method of claim 1wherein said applying said domain adaptation in said at least oneoffline training phase to reduce said divergence further comprises:reducing said divergence between a best-matched source domain and atarget domain to train one-versus-all classifiers, wherein said targetjurisdiction comprises said target domain and said best-matched sourcedomain includes said synthetic license plate character images.
 7. Themethod of claim 6 further comprising: determining said best-matchedsource domain based on a similarity between a source domain and saidtarget domain based on at least one of a distribution or a domain shiftbetween said source domain and said target domain.
 8. The method ofclaim 1 wherein said domain adaptation comprises an unsupervised domainadaptation if labeled samples are only available in a source domain andwherein said domain adaptation comprises a semi-supervised domainadaptation based on metric learning if some of said labeled samples arealso available in a target domain, said target domain comprising saidtarget jurisdiction.
 9. The method of claim 8 wherein said imagecapturing unit comprises an automated license plate recognition camera.10. A method for recognizing a license plate character, said methodcomprising: in an offline training phase, generating synthetic licenseplate character images for a target jurisdiction; in an offline trainingphase, capturing a limited set of license plate images for a targetjurisdiction utilizing an image capturing unit and thereafter segmentingsaid license plate images into license plate character images for saidtarget jurisdiction; in an offline training phase, manually labelingsaid license plate character images collected for said targetjurisdiction; in an offline training phase, applying a domain adaptationto reduce the divergence between said synthetically generated andmanually labeled target jurisdiction image sets; in an offline trainingphase, training a set of OCR classifiers using said images after saiddomain adaptation has been applied; in an online classification phase,receiving at least one input license plate character image from saidtarget jurisdiction; and utilizing said trained OCR classifiers todetermine a most likely labeling for said character image and aconfidence associated with said label, thereby minimizing manuallabeling required for training an OCR engine in a license platerecognition system.
 11. The method of claim 10 wherein said limited setof license plate images is captured utilizing an image capturing unit.12. The method of claim 10 wherein said image capturing unit comprisesan automated license plate recognition camera.
 13. A system forrecognizing a license plate character, said system comprising: at leastone processor; and a non-transitory computer-usable medium embodyingcomputer program code, said non-transitory computer-usable mediumcapable of communicating with said at least one processor, said computerprogram code comprising instructions executable by said at least oneprocessor and configured for: localizing a candidate region from regionsof interest with respect to a tag and a tag number shown in regions ofinterest within a side image of a vehicle captured by said at least onecamera; applying a domain adaptation in said at least one offlinetraining phase to reduce a divergence between a synthetic license platecharacter image synthetically generated and license plate characterimages manually labeled for a target jurisdiction; training in said atleast one offline training phase, a set of OCR classifiers using saidlicense plate character images after said domain adaptation has beenapplied; receiving in an online classification phase at least one inputlicense plate character image from said target jurisdiction; anddetermining a most likely labeling for a character image and aconfidence associated with said label utilizing said trained OCRclassifiers, thereby minimizing manual labeling required for training anOCR engine in a license plate recognition system.
 14. The system ofclaim 13 wherein said instructions are further configured for generatingin at least one offline training phase said synthetic license platecharacter images for said target jurisdiction and wherein said domainadaptation estimates a domain shift between target images of said targetjurisdiction and training images and generates a shared featurerepresentation across target samples of said target jurisdiction andsaid training samples.
 15. The system of claim 13 wherein saidinstructions are further configured for manually labeling in said atleast one offline training phase said license plate character imagescollected for said target jurisdiction.
 16. The system of claim 13wherein said instructions are further configured for capturing in saidat least one offline training phase a limited set of license plateimages for said target jurisdiction.
 17. The system of claim 16 whereinsaid instructions are further configured for thereafter segmentinglicense plate images among said limited set of license plate images intolicense plate character images for said target jurisdiction.
 18. Thesystem of claim 16 wherein said limited set of license plate images iscaptured utilizing an image capturing unit that is operably connected tosaid at least one processor.
 19. The system of claim 13 wherein saidinstructions are further configured for: capturing in said at least oneoffline training phase a limited set of license plate images for saidtarget jurisdiction; and thereafter segmenting license plate imagesamong said limited set of license plate images into license platecharacter images for said target jurisdiction.
 20. The system of claim19 wherein said limited set of license plate images is capturedutilizing an image capturing unit.